Data visualization applications enable a user to understand a data set visually, including distribution, trends, outliers, and other factors that are important to making business decisions. Some data sets are very large or complex, and include many data fields. Various tools can be used to help understand and analyze the data, including dashboards that have multiple data visualizations.
Data is commonly stored in a relational database and accessed by queries using a prescribed syntax (e.g., SQL). As volatile memory has become less expensive, database system may cache more data in memory to improve performance. Some database systems attempt to work almost entirely in memory, and take advantage of the high speed access. However, even with a large amount of available volatile memory, complex queries that access large tables of data may expand beyond the available memory. This typically leads to either aborting the execution (and no data is returned to the requester) or leads to thrashing while swapping to disk. Running out of memory is also more likely when a server is running many queries simultaneously (e.g., queries received from different users or client devices).
A database “server” can also reside on a person's computer (e.g., laptop or desktop computer), in which case the amount of memory may be much more limited than a large dedicated server. In this case, using operators optimized for in-memory execution, the database “server” may run out of memory even when the query accesses moderate sized tables and has moderate complexity. Again, the outcome is either complete failure (no results) or poor performance based on swapping memory to disk.